Guest Editors
Prof. Chih-Hsien Hsia
Email: hsiach@niu.edu.tw
Affiliation: Department of Computer Science and Information Engineering, National Ilan University, Yilan County, 26047, Taiwan
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Research Interests: computer vision, image processing, artificial intelligence, generative ai, cognitive engineering
Prof. Jing-Ming Guo
Email: jmguo@mail.ntust.edu.tw
Affiliation: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei city, 106335, Taiwan
Homepage:
Research Interests: big data signal processing, artificial intelligence, generative ai, digital image/video processing, computer vision, biometrics recognition, digital halftoning, multimedia security
Summary
Biometric recognition has seen significant advances in recent decades, playing a crucial role in security, authentication, healthcare, and human-computer interaction. Traditionally, biometric systems have relied on physiological and behavioural traits such as facial recognition, iris scanning, finger/palm vein, finger/palm print analysis, voice, gait, and behavioural. However, as systems increasingly interact with humans in real-time and emotionally dynamic environments, there is a growing need for biometrics to go beyond static identity recognition.
In recent years, the integration of Artificial Intelligence (AI) or Generative AI (GenAI), into biometric recognition systems has led to significant advancements in accuracy, robustness, and adaptability. The emergence of GenAI techniques such as GANs, diffusion models, and vision language models, the ability to synthesize, augment, and interpret biometric data has grown exponentially. These innovations not only enable enhanced performance but also raise critical concerns regarding privacy, spoofing attacks, and ethical implications. This Special Issue (SI) aims to bring together state of the art research that explores the theoretical foundations, practical applications, and future challenges associated with the use of GenAI/AI-driven biometric recognition. By offering a platform for multidisciplinary discussion, the issue will serve as a valuable reference for researchers, developers, and policymakers seeking to understand and advance the field.
Aim & Scopes:
This SI invites original research, surveys, and case studies on the integration of GenAI/AI in biometric recognition. Topics of interest include innovative AI algorithms, generative techniques for biometric data augmentation, real-time recognition systems, security and ethical concerns, and cross-modal biometric analysis. We welcome submissions that focus on theoretical frameworks, algorithmic advancements, multimodal fusion strategies, emotional context modelling, dataset creation, real-world deployments, and ethical considerations.
Keywords
Biometric recognition, Human-computer interaction, Artificial Intelligence and Generative AI
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